## Author: Kabir Khanna and Jacob Brown
rm(list=ls())

# 2020 county-level data
df <- read.csv("data/counties_analysis.csv"); head(df); dim(df)


# demographic data
acs10 = read.csv('data/ACS_county_2010.csv')
acs14 = read.csv('data/ACS_county_2014.csv')
acs18 = read.csv('data/ACS_county_2018.csv')

names(acs10) = c('id', paste0(names(acs10)[2:length(names(acs10))],'_2010'))
names(acs14) = c('id', paste0(names(acs14)[2:length(names(acs14))],'_2014'))
names(acs18) = c('id', paste0(names(acs18)[2:length(names(acs18))],'_2018'))

df$id = as.numeric(df$fips)

df = merge(df,acs10,by='id',all.x=T)
df = merge(df,acs14,by='id',all.x=T)
df = merge(df,acs18,by='id',all.x=T)

head(df); dim(df)

## Bring in 2020 eavs data
eavs = read_csv('data/2020_EAVS_for_Public_Release_nolabel[1].csv')
# make eavs variables for merge
eavs = eavs %>%
  mutate(adv20.eavs = F1f + F1d,
         fips = as.numeric(substr(FIPSCode,1,5)))%>%
  select(fips,adv20.eavs)%>%
  group_by(fips)%>%
  dplyr::summarize(adv20.eavs=sum(adv20.eavs,na.rm=T)
  )%>%
  filter(!duplicated(fips))

df = left_join(df, eavs, by = 'fips')%>%
  as.data.frame

# Model 2: regress total vote on lagged total vote

states <- unique(as.character(df$state))
states <- states[order(states)]; print(states); length(states)

reg20.fit <- as.data.frame(matrix(NA, nrow = length(states), ncol = 3)); rownames(reg20.fit) <- states; names(reg20.fit) <- c("intercept", "slope", "rmse")
reg12.fit <- reg16.fit <- reg20.fit

ratio20.fit <- as.data.frame(matrix(NA, nrow = length(states), ncol = 2)); rownames(ratio20.fit) <- states; names(ratio20.fit) <- c("ratio", "rmse")
ratio12.fit <- ratio16.fit <- ratio20.fit

reg.state.pred20 <- reg.out.rmse20 <- reg.out.bias20 <- rep(NA, length(states))
ratio.state.pred20 <- ratio.out.rmse20 <- ratio.out.bias20 <- rep(NA, length(states))
ratios.state.pred20 <- ratios.out.rmse20 <- ratios.out.bias20 <- rep(NA, length(states))

ratios.pred20 <- ratio.pred20 <- reg.pred20 <-rep(NA, nrow(df))

n.units.state.train.1216 <- rep(NA, length(states))
n.units.state.test.1620 <- rep(NA, length(states))


for (state in states) {


    if (sum(df$state == state & !is.na(df$total12) & 
            !is.na(df$total16) &
            !is.na(df$adv16.eavs) & 
             !is.na(df$reg16) & 
            !is.na(df$pct_married_2014) & 
            !is.na(df$pct_college_or_higher_2014) &
            !is.na(df$median_hh_income_2014) &
                   !is.na(df$pct_white_2014) &
                   !is.na(df$pct_black_2014) &
                   !is.na(df$pct_hispanic_2014) &
                   !is.na(df$pct_age_15_24_2014) &
                   !is.na(df$pct_age_25_34_2014) &
                   !is.na(df$pct_age_35_64_2014) &
                   !is.na(df$pct_age_65_up_2014) &
                   !is.na(df$tot_pop_2014)  ) > 1) {
      fit16.temp <- lm(total16 ~ total12 + reg16 + adv16.eavs + pct_married_2014 + pct_college_or_higher_2014 + median_hh_income_2014 + pct_white_2014 + pct_black_2014 + pct_hispanic_2014 + pct_age_15_24_2014 + pct_age_25_34_2014 + pct_age_35_64_2014 + pct_age_65_up_2014 + tot_pop_2014, df[df$state == state, ])
      n.units.state.train.1216[states == state] <- nrow(fit16.temp$model)
      
      reg16.fit$intercept[states == state] <- coef(fit16.temp)[1]
      reg16.fit$intercept.se[states == state] <- summary(fit16.temp)$coefficients['(Intercept)','Std. Error']
      
      reg16.fit$slope[states == state] <- coef(fit16.temp)[2]
      reg16.fit$slope.se[states == state] <- summary(fit16.temp)$coefficients['total12','Std. Error']
      reg16.fit$rmse[states == state] <- sqrt(mean((predict(fit16.temp) - df$total16[df$state == state & !is.na(df$total12) & 
                                                                                       !is.na(df$total16) &
                                                                                       !is.na(df$adv16.eavs) & 
                                                                                       !is.na(df$reg16) & 
                                                                                       !is.na(df$pct_married_2014) & 
                                                                                       !is.na(df$pct_college_or_higher_2014) &
                                                                                       !is.na(df$median_hh_income_2014) &
                                                                                       !is.na(df$pct_white_2014) &
                                                                                       !is.na(df$pct_black_2014) &
                                                                                       !is.na(df$pct_hispanic_2014) &
                                                                                       !is.na(df$pct_age_15_24_2014) &
                                                                                       !is.na(df$pct_age_25_34_2014) &
                                                                                       !is.na(df$pct_age_35_64_2014) &
                                                                                       !is.na(df$pct_age_65_up_2014) &
                                                                                       !is.na(df$tot_pop_2014)]) ^ 2))

      total.hat20 <- ifelse(!is.na(coef(fit16.temp)[1]),coef(fit16.temp)[1],0) + 
        ifelse(!is.na(coef(fit16.temp)[2] * df$total16[df$state == state]),coef(fit16.temp)[2] * df$total16[df$state == state],0) + 
        ifelse(!is.na(coef(fit16.temp)[3] * df$reg20[df$state == state]),coef(fit16.temp)[3] * df$reg20[df$state == state],0) +
        ifelse(!is.na(coef(fit16.temp)[4] * df$adv20.eavs[df$state == state]),coef(fit16.temp)[4] * df$adv20.eavs[df$state == state],0) +
        ifelse(!is.na(coef(fit16.temp)[5] * df$pct_married_2018[df$state == state ]),coef(fit16.temp)[5] * df$pct_married_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[6] * df$pct_college_or_higher_2018[df$state == state ]),coef(fit16.temp)[6] * df$pct_college_or_higher_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[7] * df$median_hh_income_2018[df$state == state ]),coef(fit16.temp)[7] * df$median_hh_income_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[8] * df$pct_white_2018[df$state == state ]),coef(fit16.temp)[8] * df$pct_white_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[9] * df$pct_black_2018[df$state == state ]),coef(fit16.temp)[9] * df$pct_black_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[10] * df$pct_hispanic_2018[df$state == state ]),coef(fit16.temp)[10] * df$pct_hispanic_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[11] * df$pct_age_15_24_2018[df$state == state ]),coef(fit16.temp)[11] * df$pct_age_15_24_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[12] * df$pct_age_25_34_2018[df$state == state ]),coef(fit16.temp)[12] * df$pct_age_25_34_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[13] * df$pct_age_35_64_2018[df$state == state ]),coef(fit16.temp)[13] * df$pct_age_35_64_2018[df$state == state ],0) +
        ifelse(!is.na(coef(fit16.temp)[14] * df$pct_age_65_up_2018[df$state == state ]),coef(fit16.temp)[14] * df$pct_age_65_up_2018[df$state == state ],0)+
        ifelse(!is.na(coef(fit16.temp)[15] * df$tot_pop_2018[df$state == state ]),coef(fit16.temp)[15] * df$tot_pop_2018[df$state == state ],0)+
        ifelse(!is.na(coef(fit16.temp)[16] * df$reg20[df$state == state ]),coef(fit16.temp)[16] * df$reg20[df$state == state ],0)
      
        
      n.units.state.test.1620[states == state] <- sum(!is.na(total.hat20))
      
       reg.pred20[df$state == state] <- total.hat20
      reg.out.bias20[states == state] <- mean(total.hat20 - df$total20[df$state == state & !is.na(df$total16) & !is.na(df$total20)])
      reg.out.rmse20[states == state] <- sqrt(mean((total.hat20 - df$total20[df$state == state & !is.na(df$total16) & !is.na(df$total20)]) ^ 2))
      reg.state.pred20[states == state] <- sum(total.hat20, na.rm = T)
    }
  }






holder = data.frame(bias = rep(NA,1), bias.pct = rep(NA,1), rmse = rep(NA,1), rmse.pct = rep(NA,1), Model = rep(NA,1), Training = rep(NA,1), Test = rep(NA,1), Unit = 'County')



bias.reg.all = mean(reg.pred20 - df$total20, na.rm = T); bias.reg.all.pct = bias.reg.all / mean(df$total20, na.rm = T) * 100
rmse.reg.all = sqrt(mean((reg.pred20 - df$total20) ^ 2, na.rm = T)); rmse.reg.all.pct = rmse.reg.all / mean(df$total20, na.rm = T) * 100


holder$bias[1] = bias.reg.all
holder$bias.pct[1] = bias.reg.all.pct
holder$rmse[1] = rmse.reg.all
holder$rmse.pct[1] = rmse.reg.all.pct
holder$Model[1] = 'Registration + Lagged Vote + Demographics + Early Vote'
holder$Type[1] = 'OLS'
holder$Training[1] = '2012-2016'
holder$Test[1] = '2016-2020'




##
holder = holder %>%
  mutate(n_units_training = c(sum(n.units.state.train.1216, na.rm=T)),
         n_state_training = c(sum(!is.na(n.units.state.train.1216))),
         
         n_units_test = c( sum(n.units.state.test.1620,na.rm=T)),
         n_state_test = c( sum(!is.na(n.units.state.test.1620))))




write.csv(holder, 'results/full-registration-lagged-vote-demographics-early-vote-model-county-stats-unweighted.csv')
